Epoch: 0001 train_loss= 1.41302 train_acc= 0.16602 val_loss= 1.36853 val_acc= 0.33929 time= 0.17189
Epoch: 0002 train_loss= 1.41621 train_acc= 0.18555 val_loss= 1.37491 val_acc= 0.33929 time= 0.01563
Epoch: 0003 train_loss= 1.39944 train_acc= 0.27539 val_loss= 1.38343 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38381 train_acc= 0.31250 val_loss= 1.39350 val_acc= 0.33929 time= 0.03125
Epoch: 0005 train_loss= 1.38052 train_acc= 0.29883 val_loss= 1.40301 val_acc= 0.33929 time= 0.01563
Epoch: 0006 train_loss= 1.38898 train_acc= 0.29297 val_loss= 1.41262 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.38687 train_acc= 0.29297 val_loss= 1.42295 val_acc= 0.33929 time= 0.01563
Epoch: 0008 train_loss= 1.37985 train_acc= 0.31641 val_loss= 1.43280 val_acc= 0.32143 time= 0.01563
Epoch: 0009 train_loss= 1.37376 train_acc= 0.27930 val_loss= 1.44075 val_acc= 0.32143 time= 0.01563
Epoch: 0010 train_loss= 1.37493 train_acc= 0.30469 val_loss= 1.44714 val_acc= 0.32143 time= 0.01563
Epoch: 0011 train_loss= 1.38146 train_acc= 0.31641 val_loss= 1.45305 val_acc= 0.32143 time= 0.01563
Epoch: 0012 train_loss= 1.37124 train_acc= 0.30078 val_loss= 1.45782 val_acc= 0.32143 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.38187 accuracy= 0.29204 time= 0.01563 
